Overview

Dataset statistics

Number of variables30
Number of observations1854
Missing cells109
Missing cells (%)0.2%
Duplicate rows5
Duplicate rows (%)0.3%
Total size in memory434.7 KiB
Average record size in memory240.1 B

Variable types

NUM16
CAT14

Warnings

Dataset has 5 (0.3%) duplicate rows Duplicates
Lich2 is highly correlated with Lich1High correlation
Lich1 is highly correlated with Lich2High correlation
Fstf has 109 (5.9%) missing values Missing
TempDist has 832 (44.9%) zeros Zeros
SpatDist has 1627 (87.8%) zeros Zeros
UArt1 has 62 (3.3%) zeros Zeros
AUrs1 has 1649 (88.9%) zeros Zeros
AUrs2 has 1839 (99.2%) zeros Zeros

Reproduction

Analysis started2020-10-20 18:39:55.783540
Analysis finished2020-10-20 18:40:30.075518
Duration34.29 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

TempExMax
Real number (ℝ≥0)

Distinct211
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.1359223
Minimum9
Maximum1341
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-10-20T20:40:30.135705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile24
Q169
median117
Q3218.25
95-th percentile525
Maximum1341
Range1332
Interquartile range (IQR)149.25

Descriptive statistics

Standard deviation172.9936218
Coefficient of variation (CV)0.9877677832
Kurtosis9.512961099
Mean175.1359223
Median Absolute Deviation (MAD)63
Skewness2.593921008
Sum324702
Variance29926.79317
MonotocityNot monotonic
2020-10-20T20:40:30.278052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
84351.9%
 
81351.9%
 
93341.8%
 
78331.8%
 
87321.7%
 
69321.7%
 
54321.7%
 
99311.7%
 
111311.7%
 
72311.7%
 
Other values (201)152882.4%
 
ValueCountFrequency (%) 
9100.5%
 
12110.6%
 
15110.6%
 
18261.4%
 
21211.1%
 
ValueCountFrequency (%) 
134110.1%
 
132330.2%
 
125720.1%
 
119410.1%
 
115210.1%
 

SpatExMax
Real number (ℝ≥0)

Distinct1515
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12066.22384
Minimum965
Maximum219082
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-10-20T20:40:30.412117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum965
5-th percentile1953.2
Q14497
median8426
Q314529.75
95-th percentile31323.95
Maximum219082
Range218117
Interquartile range (IQR)10032.75

Descriptive statistics

Standard deviation17030.79319
Coefficient of variation (CV)1.411443498
Kurtosis83.5097087
Mean12066.22384
Median Absolute Deviation (MAD)4636.5
Skewness8.020474636
Sum22370779
Variance290047916.7
MonotocityNot monotonic
2020-10-20T20:40:30.534291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1316380.4%
 
235160.3%
 
300060.3%
 
347560.3%
 
18973050.3%
 
662150.3%
 
929350.3%
 
1659140.2%
 
500040.2%
 
568640.2%
 
Other values (1505)180197.1%
 
ValueCountFrequency (%) 
96510.1%
 
96710.1%
 
97110.1%
 
98910.1%
 
100010.1%
 
ValueCountFrequency (%) 
21908230.2%
 
19531020.1%
 
18973050.3%
 
15323710.1%
 
13578010.1%
 

TempDist
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.264832794
Minimum0
Maximum29
Zeros832
Zeros (%)44.9%
Memory size14.5 KiB
2020-10-20T20:40:30.651159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q310
95-th percentile24
Maximum29
Range29
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.031608062
Coefficient of variation (CV)1.282014752
Kurtosis0.5580768174
Mean6.264832794
Median Absolute Deviation (MAD)3
Skewness1.257592974
Sum11615
Variance64.50672806
MonotocityNot monotonic
2020-10-20T20:40:30.764652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
083244.9%
 
6874.7%
 
7733.9%
 
5703.8%
 
8663.6%
 
9643.5%
 
3573.1%
 
10563.0%
 
4502.7%
 
12442.4%
 
Other values (20)45524.5%
 
ValueCountFrequency (%) 
083244.9%
 
1412.2%
 
2311.7%
 
3573.1%
 
4502.7%
 
ValueCountFrequency (%) 
29181.0%
 
28201.1%
 
27191.0%
 
26170.9%
 
25181.0%
 

SpatDist
Real number (ℝ≥0)

ZEROS

Distinct173
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.70010787
Minimum0
Maximum996
Zeros1627
Zeros (%)87.8%
Memory size14.5 KiB
2020-10-20T20:40:30.893651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile315.85
Maximum996
Range996
Interquartile range (IQR)0

Descriptive statistics

Standard deviation144.3555926
Coefficient of variation (CV)3.461755855
Kurtosis16.98581885
Mean41.70010787
Median Absolute Deviation (MAD)0
Skewness4.079618572
Sum77312
Variance20838.53711
MonotocityNot monotonic
2020-10-20T20:40:31.028844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0162787.8%
 
250150.8%
 
75080.4%
 
17030.2%
 
15130.2%
 
5030.2%
 
4430.2%
 
25220.1%
 
65020.1%
 
19320.1%
 
Other values (163)18610.0%
 
ValueCountFrequency (%) 
0162787.8%
 
210.1%
 
710.1%
 
1310.1%
 
1820.1%
 
ValueCountFrequency (%) 
99610.1%
 
98610.1%
 
97410.1%
 
92010.1%
 
90910.1%
 

Coverage
Real number (ℝ≥0)

Distinct97
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.06903991
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-10-20T20:40:31.165673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14
Q127
median41
Q359
95-th percentile85
Maximum100
Range98
Interquartile range (IQR)32

Descriptive statistics

Standard deviation21.30475756
Coefficient of variation (CV)0.4834404742
Kurtosis-0.3448553582
Mean44.06903991
Median Absolute Deviation (MAD)15
Skewness0.4971446998
Sum81704
Variance453.8926945
MonotocityNot monotonic
2020-10-20T20:40:31.296310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
39482.6%
 
27392.1%
 
38392.1%
 
19392.1%
 
35392.1%
 
47372.0%
 
36361.9%
 
31361.9%
 
40361.9%
 
41351.9%
 
Other values (87)147079.3%
 
ValueCountFrequency (%) 
220.1%
 
310.1%
 
430.2%
 
570.4%
 
610.1%
 
ValueCountFrequency (%) 
100221.2%
 
9720.1%
 
9650.3%
 
9510.1%
 
9480.4%
 

TimeLossCar
Real number (ℝ≥0)

Distinct803
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1504.953074
Minimum1000
Maximum1999
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-10-20T20:40:31.423757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1038.65
Q11238.5
median1519
Q31763.75
95-th percentile1950.35
Maximum1999
Range999
Interquartile range (IQR)525.25

Descriptive statistics

Standard deviation295.4587229
Coefficient of variation (CV)0.1963242097
Kurtosis-1.242004692
Mean1504.953074
Median Absolute Deviation (MAD)260
Skewness-0.06558916992
Sum2790183
Variance87295.85694
MonotocityNot monotonic
2020-10-20T20:40:31.656968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
183390.5%
 
182190.5%
 
188190.5%
 
198190.5%
 
130280.4%
 
192780.4%
 
113680.4%
 
157580.4%
 
166480.4%
 
153070.4%
 
Other values (793)177195.5%
 
ValueCountFrequency (%) 
100040.2%
 
100130.2%
 
100220.1%
 
100310.1%
 
100410.1%
 
ValueCountFrequency (%) 
199940.2%
 
199810.1%
 
199750.3%
 
199420.1%
 
199210.1%
 

TimeLossHGV
Real number (ℝ≥0)

Distinct484
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.3149946
Minimum500
Maximum998
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-10-20T20:40:31.787588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile524
Q1625
median746.5
Q3868
95-th percentile971
Maximum998
Range498
Interquartile range (IQR)243

Descriptive statistics

Standard deviation143.2553829
Coefficient of variation (CV)0.1919502943
Kurtosis-1.185045054
Mean746.3149946
Median Absolute Deviation (MAD)121.5
Skewness-0.0005985749699
Sum1383668
Variance20522.10472
MonotocityNot monotonic
2020-10-20T20:40:31.920055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
565110.6%
 
633110.6%
 
901110.6%
 
557110.6%
 
791110.6%
 
686110.6%
 
993100.5%
 
867100.5%
 
62190.5%
 
72690.5%
 
Other values (474)175094.4%
 
ValueCountFrequency (%) 
50010.1%
 
50150.3%
 
50280.4%
 
50340.2%
 
50410.1%
 
ValueCountFrequency (%) 
99850.3%
 
99730.2%
 
99620.1%
 
99540.2%
 
99420.1%
 

Strasse
Categorical

Distinct17
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
A 3
567 
A 9
456 
A 96
154 
A 7
141 
A 73
129 
Other values (12)
407 
ValueCountFrequency (%) 
A 356730.6%
 
A 945624.6%
 
A 961548.3%
 
A 71417.6%
 
A 731297.0%
 
A 61216.5%
 
A 991166.3%
 
A 92683.7%
 
A 94372.0%
 
A 70301.6%
 
Other values (7)351.9%
 
2020-10-20T20:40:32.060454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2020-10-20T20:40:32.180303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length3.309061489
Min length3

Kat
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
3
892 
7
694 
2
229 
1
 
39
ValueCountFrequency (%) 
389248.1%
 
769437.4%
 
222912.4%
 
1392.1%
 
2020-10-20T20:40:32.294584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:32.364077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:32.447612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Typ
Real number (ℝ≥0)

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-10-20T20:40:32.534198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.996489093
Coefficient of variation (CV)0.3992978186
Kurtosis-0.02277316125
Mean5
Median Absolute Deviation (MAD)0
Skewness-1.309557975
Sum9270
Variance3.985968699
MonotocityNot monotonic
2020-10-20T20:40:32.618916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
6128269.1%
 
132217.4%
 
71216.5%
 
31166.3%
 
590.5%
 
440.2%
 
ValueCountFrequency (%) 
132217.4%
 
31166.3%
 
440.2%
 
590.5%
 
6128269.1%
 
ValueCountFrequency (%) 
71216.5%
 
6128269.1%
 
590.5%
 
440.2%
 
31166.3%
 

Betei
Real number (ℝ≥0)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.276699029
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-10-20T20:40:32.712238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum18
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9891623733
Coefficient of variation (CV)0.4344721725
Kurtosis38.89614126
Mean2.276699029
Median Absolute Deviation (MAD)0
Skewness3.657730658
Sum4221
Variance0.9784422008
MonotocityNot monotonic
2020-10-20T20:40:32.795882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
2111660.2%
 
335219.0%
 
123512.7%
 
41055.7%
 
5261.4%
 
680.4%
 
760.3%
 
850.3%
 
1810.1%
 
ValueCountFrequency (%) 
123512.7%
 
2111660.2%
 
335219.0%
 
41055.7%
 
5261.4%
 
ValueCountFrequency (%) 
1810.1%
 
850.3%
 
760.3%
 
680.4%
 
5261.4%
 

UArt1
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.461165049
Minimum0
Maximum9
Zeros62
Zeros (%)3.3%
Memory size14.5 KiB
2020-10-20T20:40:32.891362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.493023605
Coefficient of variation (CV)0.7202845197
Kurtosis0.1043402037
Mean3.461165049
Median Absolute Deviation (MAD)1
Skewness1.211203796
Sum6417
Variance6.215166694
MonotocityNot monotonic
2020-10-20T20:40:32.977914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
282444.4%
 
344323.9%
 
81809.7%
 
91317.1%
 
5874.7%
 
1814.4%
 
0623.3%
 
7372.0%
 
650.3%
 
440.2%
 
ValueCountFrequency (%) 
0623.3%
 
1814.4%
 
282444.4%
 
344323.9%
 
440.2%
 
ValueCountFrequency (%) 
91317.1%
 
81809.7%
 
7372.0%
 
650.3%
 
5874.7%
 

UArt2
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2060409924
Minimum-1
Maximum9
Zeros4
Zeros (%)0.2%
Memory size14.5 KiB
2020-10-20T20:40:33.068863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.078684998
Coefficient of variation (CV)14.94209944
Kurtosis3.475909955
Mean0.2060409924
Median Absolute Deviation (MAD)0
Skewness2.299156222
Sum382
Variance9.478301317
MonotocityNot monotonic
2020-10-20T20:40:33.161247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1158285.3%
 
91317.1%
 
8754.0%
 
3402.2%
 
2120.6%
 
140.2%
 
740.2%
 
040.2%
 
510.1%
 
410.1%
 
ValueCountFrequency (%) 
-1158285.3%
 
040.2%
 
140.2%
 
2120.6%
 
3402.2%
 
ValueCountFrequency (%) 
91317.1%
 
8754.0%
 
740.2%
 
510.1%
 
410.1%
 

AUrs1
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.398597627
Minimum0
Maximum89
Zeros1649
Zeros (%)88.9%
Memory size14.5 KiB
2020-10-20T20:40:33.258521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile73
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.90578328
Coefficient of variation (CV)2.846401785
Kurtosis4.43323829
Mean8.398597627
Median Absolute Deviation (MAD)0
Skewness2.519453761
Sum15571
Variance571.486474
MonotocityNot monotonic
2020-10-20T20:40:33.352288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
0164988.9%
 
731025.5%
 
72492.6%
 
89181.0%
 
82130.7%
 
8870.4%
 
8630.2%
 
8330.2%
 
8130.2%
 
7520.1%
 
Other values (5)50.3%
 
ValueCountFrequency (%) 
0164988.9%
 
72492.6%
 
731025.5%
 
7520.1%
 
7710.1%
 
ValueCountFrequency (%) 
89181.0%
 
8870.4%
 
8710.1%
 
8630.2%
 
8510.1%
 

AUrs2
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6337648328
Minimum0
Maximum89
Zeros1839
Zeros (%)99.2%
Memory size14.5 KiB
2020-10-20T20:40:33.447420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.033430553
Coefficient of variation (CV)11.09785553
Kurtosis120.9712049
Mean0.6337648328
Median Absolute Deviation (MAD)0
Skewness11.05964402
Sum1175
Variance49.46914534
MonotocityNot monotonic
2020-10-20T20:40:33.531195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
0183999.2%
 
7350.3%
 
8130.2%
 
8420.1%
 
8020.1%
 
7520.1%
 
8910.1%
 
ValueCountFrequency (%) 
0183999.2%
 
7350.3%
 
7520.1%
 
8020.1%
 
8130.2%
 
ValueCountFrequency (%) 
8910.1%
 
8420.1%
 
8130.2%
 
8020.1%
 
7520.1%
 

AufHi
Real number (ℝ)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09816612729
Minimum-1
Maximum9
Zeros2
Zeros (%)0.1%
Memory size14.5 KiB
2020-10-20T20:40:33.616625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q33
95-th percentile3
Maximum9
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.928026845
Coefficient of variation (CV)19.64044929
Kurtosis1.031248528
Mean0.09816612729
Median Absolute Deviation (MAD)0
Skewness1.426009688
Sum182
Variance3.717287515
MonotocityNot monotonic
2020-10-20T20:40:33.708644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
-1137874.3%
 
339521.3%
 
4482.6%
 
5191.0%
 
950.3%
 
850.3%
 
020.1%
 
210.1%
 
110.1%
 
ValueCountFrequency (%) 
-1137874.3%
 
020.1%
 
110.1%
 
210.1%
 
339521.3%
 
ValueCountFrequency (%) 
950.3%
 
850.3%
 
5191.0%
 
4482.6%
 
339521.3%
 

Alkoh
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1830 
1
 
24
ValueCountFrequency (%) 
-1183098.7%
 
1241.3%
 
2020-10-20T20:40:33.904490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:33.972456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:34.047974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.987055016
Min length1

Char1
Real number (ℝ)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.4875943905
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-10-20T20:40:34.136397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile5
Maximum6
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.659526566
Coefficient of variation (CV)-3.403498069
Kurtosis7.399590635
Mean-0.4875943905
Median Absolute Deviation (MAD)0
Skewness3.019441103
Sum-904
Variance2.754028425
MonotocityNot monotonic
2020-10-20T20:40:34.227327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
-1168891.0%
 
5643.5%
 
4583.1%
 
6361.9%
 
280.4%
 
ValueCountFrequency (%) 
-1168891.0%
 
280.4%
 
4583.1%
 
5643.5%
 
6361.9%
 
ValueCountFrequency (%) 
6361.9%
 
5643.5%
 
4583.1%
 
280.4%
 
-1168891.0%
 

Char2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1810 
6
 
44
ValueCountFrequency (%) 
-1181097.6%
 
6442.4%
 
2020-10-20T20:40:34.340355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:34.406244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:34.476664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.97626753
Min length1

Bes1
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1512 
6
335 
1
 
7
ValueCountFrequency (%) 
-1151281.6%
 
633518.1%
 
170.4%
 
2020-10-20T20:40:34.586881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:34.662425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:34.742798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.815533981
Min length1

Bes2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1852 
6
 
2
ValueCountFrequency (%) 
-1185299.9%
 
620.1%
 
2020-10-20T20:40:34.843303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:34.915559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:34.984882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.998921251
Min length1

Lich1
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
0
1466 
2
291 
1
 
95
-1
 
2
ValueCountFrequency (%) 
0146679.1%
 
229115.7%
 
1955.1%
 
-120.1%
 
2020-10-20T20:40:35.092446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:35.172381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:35.260455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.001078749
Min length1

Lich2
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1468 
4
370 
3
 
16
ValueCountFrequency (%) 
-1146879.2%
 
437020.0%
 
3160.9%
 
2020-10-20T20:40:35.373328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:35.447962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:35.530281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.79180151
Min length1

Zust1
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
0
1391 
1
411 
2
 
48
-1
 
4
ValueCountFrequency (%) 
0139175.0%
 
141122.2%
 
2482.6%
 
-140.2%
 
2020-10-20T20:40:35.642691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:35.721418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:35.805421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.002157497
Min length1

Zust2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1835 
2
 
19
ValueCountFrequency (%) 
-1183599.0%
 
2191.0%
 
2020-10-20T20:40:35.913584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:35.983099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:36.057988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.989751888
Min length1

Fstf
Categorical

MISSING

Distinct7
Distinct (%)0.4%
Missing109
Missing (%)5.9%
Memory size14.5 KiB
2
800 
1
578 
3
297 
4
 
36
S
 
26
Other values (2)
 
8
ValueCountFrequency (%) 
280043.1%
 
157831.2%
 
329716.0%
 
4361.9%
 
S261.4%
 
550.3%
 
F30.2%
 
(Missing)1095.9%
 
2020-10-20T20:40:36.172128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:36.353242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:36.468151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.117583603
Min length1

StrklVu
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
A
1849 
G
 
4
B
 
1
ValueCountFrequency (%) 
A184999.7%
 
G40.2%
 
B10.1%
 
2020-10-20T20:40:36.592081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-10-20T20:40:36.669804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:36.744806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

WoTag
Real number (ℝ≥0)

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.080906149
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-10-20T20:40:36.827077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.89694902
Coefficient of variation (CV)0.4648352476
Kurtosis-1.183085467
Mean4.080906149
Median Absolute Deviation (MAD)2
Skewness-0.09682627444
Sum7566
Variance3.598415584
MonotocityNot monotonic
2020-10-20T20:40:36.911940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
634818.8%
 
528315.3%
 
428315.3%
 
226714.4%
 
326514.3%
 
120911.3%
 
719910.7%
 
ValueCountFrequency (%) 
120911.3%
 
226714.4%
 
326514.3%
 
428315.3%
 
528315.3%
 
ValueCountFrequency (%) 
719910.7%
 
634818.8%
 
528315.3%
 
428315.3%
 
326514.3%
 

FeiTag
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1806 
1
 
48
ValueCountFrequency (%) 
-1180697.4%
 
1482.6%
 
2020-10-20T20:40:37.024089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:37.096240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:37.171393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.974110032
Min length1

Month
Categorical

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
Jul
232 
Aug
216 
Sep
169 
Oct
161 
Jun
157 
Other values (7)
919 
ValueCountFrequency (%) 
Jul23212.5%
 
Aug21611.7%
 
Sep1699.1%
 
Oct1618.7%
 
Jun1578.5%
 
Apr1488.0%
 
Mar1427.7%
 
May1397.5%
 
Nov1397.5%
 
Dec1377.4%
 
Other values (2)21411.5%
 
2020-10-20T20:40:37.288373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T20:40:37.397569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

2020-10-20T20:40:00.023735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:00.148442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:00.256376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:00.369522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:00.488523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:00.602925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:00.726695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:00.843675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:00.954019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:01.066118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:01.200224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:01.331530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:01.445156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:01.556716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:01.673671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:01.808271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:01.926604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:02.052115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:02.162924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:02.273454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:02.381515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:02.496547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:02.612299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:02.728996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:02.837733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:02.944448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:03.064489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:03.168988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:03.371894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:03.475696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:03.571680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:03.678671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:03.780200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:03.895538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:04.015466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:04.129893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:04.243015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:04.357764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:04.466219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:04.583380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:04.699767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:04.814932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:04.930709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:05.035217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:05.145885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:05.267640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:05.376475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:05.516520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:05.659570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:05.793448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:05.914139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:06.039929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:06.169146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:06.299913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:06.419267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:06.538226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:06.648673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:06.867769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:06.978000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:07.099475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:07.208815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:07.307752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:07.411932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:07.531980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:07.653467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:07.771203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:07.881539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:07.998106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:08.115543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:08.230755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:08.346799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:08.449924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:08.559808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:08.676293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:08.780074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:08.883056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:08.982827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:09.083228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:09.187740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:09.290648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:09.394740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:09.504307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:09.604205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:09.712752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:09.814392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:09.925357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:10.135632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:10.249689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:10.364856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:10.474857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:10.574714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:10.678256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:10.776701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:10.879343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:10.986779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:11.101323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:11.223192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:11.350479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:11.465436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:11.574347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:11.684059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:11.787518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:11.893282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:12.006022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:12.115146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:12.222063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:12.320768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:12.421690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:12.525918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:12.631439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:12.736261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:12.844051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:12.950062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:13.066515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:13.174687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:13.388790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:13.504351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:13.610999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:13.719130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:13.827096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:13.935475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:14.044350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:14.143994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:14.247822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:14.348352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:14.451986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:14.551202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:14.673681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:14.777610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:14.889045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:14.995872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:15.110169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:15.224331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:15.330754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:15.442682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:15.556851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:15.668548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:15.788888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:15.894476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:15.998762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:16.108937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:16.218738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:16.322767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:16.537264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:16.656949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:16.764749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:16.859813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:16.961918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:17.072308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:17.175270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:17.274055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:17.372203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:17.478405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:17.579361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:17.676878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:17.769154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:17.862694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:17.961013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:18.054457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:18.157678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:18.263068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:18.365156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:18.463004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:18.560209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:18.662099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:18.761841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:18.860894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:18.973979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:19.092336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:19.197047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:19.290116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:19.384940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:19.480218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:19.681572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:19.783354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:19.887557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:19.988094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:20.089506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:20.183034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:20.280701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:20.376338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:20.483034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:20.579486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:20.678439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:20.774354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:20.870981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:20.965202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:21.057836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:21.156058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:21.248922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:21.342278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:21.444879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:21.543885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:21.644510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:21.750768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:21.851256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:21.950843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:22.056064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:22.158986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:22.255292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:22.353660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:22.460734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:22.554707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:22.759652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:22.861394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:22.962534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:23.057928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:23.161124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:23.262298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:23.366270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:23.465995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:23.565295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:23.662298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:23.761221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:23.858037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:23.958498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:24.055493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:24.154150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:24.249151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:24.345532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:24.447151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:24.544733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:24.642871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:24.752328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:24.852172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:24.972909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:25.080073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:25.190869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:25.299341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T20:40:25.527878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:25.639498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:25.856814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:25.979918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:26.080909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:26.185465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:26.292057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:26.397112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:26.503537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:26.617531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:26.732691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:26.842178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:26.945066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:27.066583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:27.176167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:27.278762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:27.382884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:27.494396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:27.603108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:27.714121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:27.814599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:27.912226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:28.016484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:28.123017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:28.227861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:28.336159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-20T20:40:37.526120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-20T20:40:37.813430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-20T20:40:38.096866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-20T20:40:38.391624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-20T20:40:38.671017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-20T20:40:28.601962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:29.671934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T20:40:29.895984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

TempExMaxSpatExMaxTempDistSpatDistCoverageTimeLossCarTimeLossHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfStrklVuWoTagFeiTagMonth
0360656800611714718A 32132-100-1-1-1-1-1-10-11-12A31Jan
1696000100381691686A 63632-1890-1-1-1-1-1-10-10-12A31Jan
21621392500471389947A 336529003-1-1-1-1-10-11-12A4-1Jan
3162139251996471389947A 33672-1820-1-1-1-1-1-10-11-12A4-1Jan
41622070100291657905A 33622-100-1-1-1-1-1-10-10-1NaNA4-1Jan
545748300401905988A 63632-100-11-1-1-1-10-10-11A4-1Jan
62851806700391407745A 97133-1720-1-1-1-1-1-10-1121A4-1Jan
721612991110431923670A 33733-100-1-1-1-1-1-1241-12A5-1Jan
8138641500391355882A 97123-100-1-1-1-1-1-1240-11A6-1Jan
910599410112261557641A 93632-100-1-1-1-1-1-1241-14A6-1Jan

Last rows

TempExMaxSpatExMaxTempDistSpatDistCoverageTimeLossCarTimeLossHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfStrklVuWoTagFeiTagMonth
1844961628230641881888A 62622-100-1-1-1-1-1-10-10-12A6-1Dec
184581269880721048823A 73119-1003-16-1-1-1240-12A6-1Dec
18462372922900211046757A 93632-100-1-1-1-1-1-10-10-1FA6-1Dec
18475644324400161781998A 731429003-1-1-1-1-10-10-12A7-1Dec
184820114999160371059947A 73622-100-1-1-1-1-1-10-10-11A7-1Dec
1849453955220551414799A 93622-100-1-1-1-1-1-10-10-11A1-1Dec
18503913456110731561865A 93734-100-1-1-1-1-1-1240-13A1-1Dec
1851135648112750381002683A 97622-100-1-1-1-16-10-10-11A1-1Dec
185287378800871580707A 923622-100-11-1-1-1-1240-12A1-1Dec
185393341840751714958A 33632-100-1-1-1-1-1-1230-12A2-1Dec

Duplicate rows

Most frequent

TempExMaxSpatExMaxTempDistSpatDistCoverageTimeLossCarTimeLossHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfStrklVuWoTagFeiTagMonthcount
0991307900421592969A 97110-100-1-1-1-1-1-10-10-1SA7-1Jul2
11773049600161313519A 37325-100-1-1-1-1-1-10-11-12A5-1May2
22522079200191691686A 33622-100-1-1-1-1-1-10-10-12A1-1Sep2
3294766000311651814A 37625-100-1-1-1-16-10-10-12A5-1Aug2
4450957400371833938A 97623-100-1-1-1-16-10-10-12A6-1Jun2